Feature Point Detection and Visual Location Based on Distinctiveness Evaluation
Traditional feature point detection algorithms are difficult to cope with the changes in lighting and viewpoint in the real scenes.The positioning accuracy of feature points obtained by the feature point detection algorithm based on deep learning is insufficient,and it is difficult to eliminate similar feature points in local areas.In order to improve the performance of algorithm based on deep learning,a feature point detection algorithm based on feature fusion and uniqueness evaluation is designed.Firstly,in order to improve the positioning accuracy of feature points,the network structure based on feature fusion and the corresponding feature fusion loss function are used to solve the problems of detail feature offset and blur in high-level features.Secondly,whether the feature points are from locally similar regions is converted into the uniqueness evaluation of the feature points,the uniqueness branch is added to the network structure,and the uniqueness loss function is designed to learn the uniqueness response value of the pixels in the predicted image.By extracting feature points with high uniqueness response values,the feature points of locally similar regions are excluded to reduce the number of mis-matched in subsequent feature matches.Based on the algorithm,the visual odometry and visual simultaneous localization and mapping system are constructed,and the system has good robustness and accurate positioning ability in large-scale outdoor scenes and small-scale indoor scenes on the KITTI and TUM datasets.
artificial intelligencefeature point detectiondeep learningvisual odometrysimultaneous localization and mapping
国家科技期刊平台
NSTL
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